Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines

Souza, Renato William R. de; Silva, Daniel S.; Passos, Leandro Aparecido; Roder, Mateus; Santana, Marcos Cleison Silva; Pinheiro, Plácido Rogério; Albuquerque, Victor Hugo C. de

doi:10.1016/j.compbiomed.2021.104260

Cited by 39 publications

(16 citation statements)

References 21 publications

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“…The building blocks of DBNs are RBMs, which are shallow two-layer neural nets. The visible, or input layer, is the first layer of the RBM, and the secret layer is the second [ 48 ]. So, DL may be used in various fields that require the processing of large amounts of data [ 49 ].…”

Section: Basic Concepts and Corresponding Terminologiesmentioning

confidence: 99%

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Heidari¹,

Navimipour²,

Ünal³

et al. 2022

Computers in Biology and Medicine

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Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.

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Section: Basic Concepts and Corresponding Terminologiesmentioning

confidence: 99%

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Heidari¹,

Navimipour²,

Ünal³

et al. 2022

Computers in Biology and Medicine

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“…Deep Belief Networks (DBNs) [31] are stochastic neural networks constructed using Restricted Boltzmann Machines [30] as building blocks. Such models became very popular due to their ability of performing several tasks, such as feature selection [15], classification [81], and image reconstruction [73,74], among others. Regarding gait recognition, Fernandes et al [21] employed DBNs to support gait assessment in the diagnosis of Parkinson's and movement disorder diseases.…”

Section: Deep Belief Networkmentioning

confidence: 99%

Gait Recognition Based on Deep Learning: A Survey

et al. 2022

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In general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately. Instead, such systems should be aware of malicious procedures for unauthorized access attempts. Some works available in the literature suggest addressing the problem through gait recognition approaches. Such methods aim at identifying human beings through intrinsic perceptible features, despite dressed clothes or accessories. Although the issue denotes a relatively long-time challenge, most of the techniques developed to handle the problem present several drawbacks related to feature extraction and low classification rates, among other issues. However, deep learning-based approaches recently emerged as a robust set of tools to deal with virtually any image and computer-vision-related problem, providing paramount results for gait recognition as well. Therefore, this work provides a surveyed compilation of recent works regarding biometric detection through gait recognition with a focus on deep learning approaches, emphasizing their benefits and exposing their weaknesses. Besides, it also presents categorized and characterized descriptions of the datasets, approaches, and architectures employed to tackle associated constraints.

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“…In a study by Balaji et al [ 49 ], a DL approach based on LSTM was developed for severity classification of the PD and presented encouraging results with an overall accuracy value of 98.6%. De Souza et al [ 50 ] presented a Fuzzy OPF for PD diagnosis. RBM was used to extract the features and outperformed other baseline models.…”

Section: Related Workmentioning

confidence: 99%

Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning

Nilashi

Abumalloh

Minaei‐Bidgoli

et al. 2022

Journal of Healthcare Engineering

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Parkinson’s disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.

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Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines

Cited by 39 publications

References 21 publications

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Gait Recognition Based on Deep Learning: A Survey

Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning

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